In recent years, deep learning (DL) has shown impressive performance in radiologic image
analysis. However, for a DL model to be useful in a real-world setting, its confidence in a …

The increasing use of machine learning (ML) algorithms in clinical settings raises concerns
about bias in ML models. Bias can arise at any step of ML creation, including data handling …

A Food and Drug Administration (FDA)–cleared artificial intelligence (AI) algorithm
misdiagnosed a finding as an intracranial hemorrhage in a patient, who was finally …

Artificial intelligence (AI) describes the application of computer algorithms to the solution of
problems that have traditionally required human intelligence. Although formal work in AI has …

OBJECTIVE Gliomas exhibit high intratumor and interpatient heterogeneity. Recently, it has
been shown that the microenvironment and phenotype differ significantly between the …

Management of patients with glioblastoma (GBM) is complex and involves implementing
standard therapies including resection, radiation therapy, and chemotherapy, as well as …

Purpose Distinguishing stage 1–2 adrenocortical carcinoma (ACC) and large, lipid poor
adrenal adenoma (LPAA) via imaging is challenging due to overlap** imaging …

This comprehensive review explores the role of deep learning (DL) in glioma segmentation
using multiparametric magnetic resonance imaging (MRI) data. The study surveys advanced …

Abstract Purpose of Review The neuro-oncology team faces a unique challenge when
assessing treatment response in patients diagnosed with glioblastoma. Magnetic resonance …

Intratumoral heterogeneity poses a significant challenge to the diagnosis and treatment of
recurrent glioblastoma. This study addresses the need for non-invasive approaches to map …